| 書目名稱 | Research in Data Science |
| 編輯 | Ellen Gasparovic,Carlotta Domeniconi |
| 視頻video | http://file.papertrans.cn/828/827974/827974.mp4 |
| 概述 | Highlights the fundamental role of mathematics in the development of data science.Features a diverse range of topics from the theoretical to the applied and computational.Based on the 2017 Women in Da |
| 叢書名稱 | Association for Women in Mathematics Series |
| 圖書封面 |  |
| 描述 | This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and data science, this book addresses cutting edge problems in predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas.? Contributions study topics such as the hubness phenomenon in high-dimensional spaces, the use of a heuristic framework for testing the multi-manifold hypothesis for high-dimensional data, the investigation of interdisciplinary approaches to multi-dimensional obstructive sleep apnea patient data, and the inference of a dyadic measure and its simplicial geometry from binary feature data.?.Based on the first Women in Data Science and Mathematics (WiSDM) Research Collaboration Workshop that took place in 2017 at the Institute for Compuational and Experimental Research in Mathematics (ICERM) in Providence, Rhode Island, this volume features submissions from several of the working groups as well as contributions from the wider community.? The volume is suitable for researchers in data science in industry and a |
| 出版日期 | Book 2019 |
| 關(guān)鍵詞 | data science; data analysis; multiple measurement vectors; statistical and topological inference; hubnes |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-11566-1 |
| isbn_ebook | 978-3-030-11566-1Series ISSN 2364-5733 Series E-ISSN 2364-5741 |
| issn_series | 2364-5733 |
| copyright | The Author(s) and the Association for Women in Mathematics 2019 |
|Archiver|手機(jī)版|小黑屋|
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